Data Engineering

Schaeffler es una empresa de tecnología global dinámica y su éxito ha sido resultado de su espíritu empresarial y su larga historia de propiedad privada. ¿Le parece interesante? Como socio de los principales fabricantes de automóviles, así como de los principales actores en los sectores aeroespacial e industrial, le ofrecemos muchas oportunidades de desarrollo. Goal of Position Data Engineering covers the collection, modeling, storage, processing and evaluation of extremely large (e.g. social media), rapidly changing (e.g. sensor data) and complex (e.g. ecological systems) data sets. ETL: Extract, Transform, Load (data). Data sets may origin and/or concern all information processes in business. The term complex refers to massive, interrelated and dynamic nature of data sets to be analyzed. Your Qualifications Graduate Degree in Computer Science or Information Systems, Applied Statistics or Mathematics, Software Engineering. 3 to 5 years of working experience. Your Key Responsibilities Develop data pipelines and apply methods and tools to collect, store, process and analyze complex data sets, globally for assigned operations or functions. Design, govern, build and operate solutions for large-scale data architectures and applications across businesses and functions. Select, manage and work hands‑on with big data tools and frameworks, and implement ETL (extract, transform, load) tools and processes as well as data virtualization and federation services. Engineer data integration pipelines and reusable data services using cross‑functional data models, semantic technologies and data integration solutions. Define, implement and apply data governance policies for all data flows of data architectures with focus on the digital platform and data lake. Define and implement policies for data ingestion, retention, lineage, access, data service API management and usage, in collaboration with data management and IT functions. Contribution towards the continuous improvement of our data capabilities. Conduct business meetings with stakeholders to determine project requirements and milestones. Enable user self‑service on data transformation and data visualization using Power BI. Collaborate with other IT teams to develop end‑to‑end custom data science solutions under agile framework. Design and implement large‑scale data solutions using cloud technologies providing reliable data services for reporting, self‑service BI or custom solutions taking data plausibility and performance into account. Su contacto Schaeffler Transmision, S. de R.L. de C. Ana Paula Lechuga www.schaeffler.com/careers #J-18808-Ljbffr